code: def run_single_example(len_forecast=40, visualization=False):
      ts_name = "sea_level"
      path_to_file = "../../cases/data/nemo/sea_surface_height.csv"
      path_to_exog_file = (
          "../../cases/data/nemo/sea_surface_height_nemo.csv"
      )
  
      df = pd.read_csv(path_to_file)
      time_series = df[ts_name]
      df = pd.read_csv(path_to_exog_file)
      exog_variable = df[ts_name]
  
      run_nemo_based_forecasting(
          time_series=time_series,
          exog_variable=exog_variable,
          len_forecast=len_forecast,
          visualization=visualization,
      )
  
imports: import os
  from copy import deepcopy
  import numpy
  import pandas
  from matplotlib import pyplot
  from sklearn.metrics import mean_squared_error, mean_absolute_error
  from fedot.core.data.data import InputData
  from fedot.core.data.data_split import train_test_data_setup
  from fedot.core.data.multi_modal import MultiModalData
  from fedot.core.pipelines.node import PipelineNode
  from fedot.core.pipelines.pipeline import Pipeline
  from fedot.core.repository.dataset_types import DataTypesEnum
  from fedot.core.repository.tasks import Task, TsForecastingParams, TaskTypesEnum
label_desc: Use image node_id 12 for calling a global function with example usage: run_single_example(len_forecast, visualization) without return types